Overview

Dataset statistics

Number of variables29
Number of observations208829
Missing cells655342
Missing cells (%)10.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.2 MiB
Average record size in memory232.0 B

Variable types

Numeric12
Categorical7
Unsupported3
DateTime2
Text5

Alerts

Sample Duration is highly imbalanced (98.7%)Imbalance
Pollutant Standard has 208829 (100.0%) missing valuesMissing
Event Type has 208829 (100.0%) missing valuesMissing
AQI has 208829 (100.0%) missing valuesMissing
Local Site Name has 4753 (2.3%) missing valuesMissing
CBSA Name has 23740 (11.4%) missing valuesMissing
Pollutant Standard is an unsupported type, check if it needs cleaning or further analysisUnsupported
Event Type is an unsupported type, check if it needs cleaning or further analysisUnsupported
AQI is an unsupported type, check if it needs cleaning or further analysisUnsupported
1st Max Hour has 11276 (5.4%) zerosZeros

Reproduction

Analysis started2024-02-14 01:08:42.008838
Analysis finished2024-02-14 01:09:00.160051
Duration18.15 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

State Code
Real number (ℝ)

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.897701
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:00.235641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median26
Q341
95-th percentile55
Maximum56
Range55
Interquartile range (IQR)33

Descriptive statistics

Standard deviation16.781033
Coefficient of variation (CV)0.64797385
Kurtosis-1.2399323
Mean25.897701
Median Absolute Deviation (MAD)17
Skewness0.23359368
Sum5408191
Variance281.60307
MonotonicityIncreasing
2024-02-13T20:09:00.372953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
6 30874
 
14.8%
48 21448
 
10.3%
4 11734
 
5.6%
26 11574
 
5.5%
29 11048
 
5.3%
19 9074
 
4.3%
8 9008
 
4.3%
24 8712
 
4.2%
32 8227
 
3.9%
41 7185
 
3.4%
Other values (37) 79945
38.3%
ValueCountFrequency (%)
1 362
 
0.2%
2 3380
 
1.6%
4 11734
 
5.6%
5 545
 
0.3%
6 30874
14.8%
8 9008
 
4.3%
9 3660
 
1.8%
11 360
 
0.2%
12 1058
 
0.5%
13 5062
 
2.4%
ValueCountFrequency (%)
56 5253
 
2.5%
55 5858
 
2.8%
54 286
 
0.1%
53 4559
 
2.2%
51 952
 
0.5%
49 1639
 
0.8%
48 21448
10.3%
47 2100
 
1.0%
46 498
 
0.2%
45 1215
 
0.6%

County Code
Real number (ℝ)

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.672531
Minimum1
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:00.468574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q119
median53
Q3103
95-th percentile209
Maximum510
Range509
Interquartile range (IQR)84

Descriptive statistics

Standard deviation81.70806
Coefficient of variation (CV)1.0797585
Kurtosis8.2023492
Mean75.672531
Median Absolute Deviation (MAD)38
Skewness2.4685042
Sum15802619
Variance6676.2071
MonotonicityNot monotonic
2024-02-13T20:09:00.559580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 11095
 
5.3%
3 11018
 
5.3%
27 8386
 
4.0%
71 6877
 
3.3%
19 6769
 
3.2%
5 6389
 
3.1%
29 5464
 
2.6%
9 4797
 
2.3%
79 4502
 
2.2%
39 4394
 
2.1%
Other values (102) 139138
66.6%
ValueCountFrequency (%)
1 3876
 
1.9%
3 11018
5.3%
5 6389
3.1%
7 2473
 
1.2%
9 4797
2.3%
11 945
 
0.5%
13 11095
5.3%
15 2724
 
1.3%
17 3298
 
1.6%
19 6769
3.2%
ValueCountFrequency (%)
510 900
0.4%
493 362
 
0.2%
479 362
 
0.2%
439 1356
0.6%
397 346
 
0.2%
395 358
 
0.2%
375 846
0.4%
367 360
 
0.2%
349 358
 
0.2%
339 362
 
0.2%

Site Num
Real number (ℝ)

Distinct217
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100.0001
Minimum1
Maximum9997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:00.648115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median43
Q31013
95-th percentile9001
Maximum9997
Range9996
Interquartile range (IQR)1004

Descriptive statistics

Standard deviation2339.1881
Coefficient of variation (CV)2.1265344
Kurtosis6.1334495
Mean1100.0001
Median Absolute Deviation (MAD)41
Skewness2.6925803
Sum2.2971193 × 108
Variance5471801.1
MonotonicityNot monotonic
2024-02-13T20:09:00.734509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 8285
 
4.0%
4 7999
 
3.8%
1 7276
 
3.5%
2 6224
 
3.0%
7 5604
 
2.7%
5 5510
 
2.6%
9003 5482
 
2.6%
9 5200
 
2.5%
101 5044
 
2.4%
6 4396
 
2.1%
Other values (207) 147809
70.8%
ValueCountFrequency (%)
1 7276
3.5%
2 6224
3.0%
3 8285
4.0%
4 7999
3.8%
5 5510
2.6%
6 4396
2.1%
7 5604
2.7%
8 2668
 
1.3%
9 5200
2.5%
10 2321
 
1.1%
ValueCountFrequency (%)
9997 498
 
0.2%
9812 362
 
0.2%
9704 362
 
0.2%
9702 362
 
0.2%
9009 362
 
0.2%
9008 964
 
0.5%
9007 362
 
0.2%
9003 5482
2.6%
9002 1210
 
0.6%
9001 1863
 
0.9%

Parameter Code
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
61104
104898 
61103
103931 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1044145
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row61103
2nd row61103
3rd row61103
4th row61103
5th row61103

Common Values

ValueCountFrequency (%)
61104 104898
50.2%
61103 103931
49.8%

Length

2024-02-13T20:09:00.806723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-13T20:09:00.972598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
61104 104898
50.2%
61103 103931
49.8%

Most occurring characters

ValueCountFrequency (%)
1 417658
40.0%
6 208829
20.0%
0 208829
20.0%
4 104898
 
10.0%
3 103931
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1044145
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 417658
40.0%
6 208829
20.0%
0 208829
20.0%
4 104898
 
10.0%
3 103931
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1044145
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 417658
40.0%
6 208829
20.0%
0 208829
20.0%
4 104898
 
10.0%
3 103931
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1044145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 417658
40.0%
6 208829
20.0%
0 208829
20.0%
4 104898
 
10.0%
3 103931
 
10.0%

POC
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2274349
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:01.052339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum14
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1803264
Coefficient of variation (CV)0.96162036
Kurtosis65.280031
Mean1.2274349
Median Absolute Deviation (MAD)0
Skewness7.708825
Sum256324
Variance1.3931704
MonotonicityNot monotonic
2024-02-13T20:09:01.165560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 190468
91.2%
2 12129
 
5.8%
3 2348
 
1.1%
4 362
 
0.2%
5 362
 
0.2%
6 362
 
0.2%
7 362
 
0.2%
8 362
 
0.2%
9 362
 
0.2%
10 360
 
0.2%
Other values (4) 1352
 
0.6%
ValueCountFrequency (%)
1 190468
91.2%
2 12129
 
5.8%
3 2348
 
1.1%
4 362
 
0.2%
5 362
 
0.2%
6 362
 
0.2%
7 362
 
0.2%
8 362
 
0.2%
9 362
 
0.2%
10 360
 
0.2%
ValueCountFrequency (%)
14 318
0.2%
13 332
0.2%
12 346
0.2%
11 356
0.2%
10 360
0.2%
9 362
0.2%
8 362
0.2%
7 362
0.2%
6 362
0.2%
5 362
0.2%

Latitude
Real number (ℝ)

Distinct564
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.84807
Minimum19.4308
Maximum64.84593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:01.305809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19.4308
5-th percentile30.229653
Q135.03146
median38.63114
Q342.295824
95-th percentile47.22634
Maximum64.84593
Range45.41513
Interquartile range (IQR)7.264364

Descriptive statistics

Standard deviation5.753935
Coefficient of variation (CV)0.14811379
Kurtosis4.4046952
Mean38.84807
Median Absolute Deviation (MAD)3.61031
Skewness1.1495656
Sum8112603.7
Variance33.107768
MonotonicityNot monotonic
2024-02-13T20:09:01.390138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.5486 4970
 
2.4%
64.762641 1086
 
0.5%
38.11999 964
 
0.5%
29.30265 848
 
0.4%
64.84569 724
 
0.3%
64.84593 724
 
0.3%
41.634104 724
 
0.3%
35.642943 722
 
0.3%
41.401459 546
 
0.3%
40.969112 546
 
0.3%
Other values (554) 196975
94.3%
ValueCountFrequency (%)
19.4308 486
0.2%
27.422433 26
 
< 0.1%
27.501826 362
0.2%
27.96565 344
0.2%
28.88044 360
0.2%
28.964394 362
0.2%
29.1307 362
0.2%
29.162997 362
0.2%
29.254474 62
 
< 0.1%
29.275381 356
0.2%
ValueCountFrequency (%)
64.84593 724
0.3%
64.84569 724
0.3%
64.762641 1086
0.5%
63.7232 486
0.2%
58.388497 360
 
0.2%
48.848065 362
 
0.2%
48.64193 406
 
0.2%
48.544448 362
 
0.2%
48.5103 468
0.2%
48.41252 442
0.2%

Longitude
Real number (ℝ)

Distinct564
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-100.42812
Minimum-155.2578
Maximum-67.615495
Zeros0
Zeros (%)0.0%
Negative208829
Negative (%)100.0%
Memory size1.6 MiB
2024-02-13T20:09:01.472916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-155.2578
5-th percentile-122.30863
Q1-115.25333
median-97.431052
Q3-87.321667
95-th percentile-75.797317
Maximum-67.615495
Range87.642305
Interquartile range (IQR)27.931666

Descriptive statistics

Standard deviation16.523901
Coefficient of variation (CV)-0.1645346
Kurtosis-0.38737006
Mean-100.42812
Median Absolute Deviation (MAD)13.602115
Skewness-0.26632568
Sum-20972304
Variance273.0393
MonotonicityNot monotonic
2024-02-13T20:09:01.579052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-90.83725 4970
 
2.4%
-147.310279 1086
 
0.5%
-90.28214 964
 
0.5%
-103.17781 848
 
0.4%
-147.727413 724
 
0.3%
-147.69327 724
 
0.3%
-87.101452 724
 
0.3%
-117.715066 722
 
0.3%
-91.068449 546
 
0.3%
-95.044951 546
 
0.3%
Other values (554) 196975
94.3%
ValueCountFrequency (%)
-155.2578 486
0.2%
-148.9676 486
0.2%
-147.727413 724
0.3%
-147.69327 724
0.3%
-147.310279 1086
0.5%
-134.567237 360
 
0.2%
-124.62491 362
 
0.2%
-123.348466 424
 
0.2%
-123.1211 546
0.3%
-123.017704 546
0.3%
ValueCountFrequency (%)
-67.615495 542
0.3%
-68.22698 410
0.2%
-68.2609 544
0.3%
-70.713958 244
0.1%
-70.748017 354
0.2%
-71.0826 362
0.2%
-71.36097 362
0.2%
-71.38014 272
0.1%
-71.412968 362
0.2%
-71.423705 362
0.2%

Datum
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
WGS84
139070 
NAD83
69759 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1044145
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNAD83
2nd rowNAD83
3rd rowNAD83
4th rowNAD83
5th rowNAD83

Common Values

ValueCountFrequency (%)
WGS84 139070
66.6%
NAD83 69759
33.4%

Length

2024-02-13T20:09:01.653236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-13T20:09:01.728877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wgs84 139070
66.6%
nad83 69759
33.4%

Most occurring characters

ValueCountFrequency (%)
8 208829
20.0%
W 139070
13.3%
G 139070
13.3%
S 139070
13.3%
4 139070
13.3%
N 69759
 
6.7%
A 69759
 
6.7%
D 69759
 
6.7%
3 69759
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 626487
60.0%
Decimal Number 417658
40.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 139070
22.2%
G 139070
22.2%
S 139070
22.2%
N 69759
11.1%
A 69759
11.1%
D 69759
11.1%
Decimal Number
ValueCountFrequency (%)
8 208829
50.0%
4 139070
33.3%
3 69759
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 626487
60.0%
Common 417658
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 139070
22.2%
G 139070
22.2%
S 139070
22.2%
N 69759
11.1%
A 69759
11.1%
D 69759
11.1%
Common
ValueCountFrequency (%)
8 208829
50.0%
4 139070
33.3%
3 69759
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1044145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 208829
20.0%
W 139070
13.3%
G 139070
13.3%
S 139070
13.3%
4 139070
13.3%
N 69759
 
6.7%
A 69759
 
6.7%
D 69759
 
6.7%
3 69759
 
6.7%

Parameter Name
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Wind Direction - Resultant
104898 
Wind Speed - Resultant
103931 

Length

Max length26
Median length26
Mean length24.009261
Min length22

Characters and Unicode

Total characters5013830
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWind Speed - Resultant
2nd rowWind Speed - Resultant
3rd rowWind Speed - Resultant
4th rowWind Speed - Resultant
5th rowWind Speed - Resultant

Common Values

ValueCountFrequency (%)
Wind Direction - Resultant 104898
50.2%
Wind Speed - Resultant 103931
49.8%

Length

2024-02-13T20:09:01.809759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-13T20:09:01.889660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wind 208829
25.0%
208829
25.0%
resultant 208829
25.0%
direction 104898
12.6%
speed 103931
12.4%

Most occurring characters

ValueCountFrequency (%)
626487
12.5%
t 522556
10.4%
n 522556
10.4%
e 521589
10.4%
i 418625
 
8.3%
d 312760
 
6.2%
R 208829
 
4.2%
a 208829
 
4.2%
l 208829
 
4.2%
u 208829
 
4.2%
Other values (9) 1253941
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3552027
70.8%
Space Separator 626487
 
12.5%
Uppercase Letter 626487
 
12.5%
Dash Punctuation 208829
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 522556
14.7%
n 522556
14.7%
e 521589
14.7%
i 418625
11.8%
d 312760
8.8%
a 208829
 
5.9%
l 208829
 
5.9%
u 208829
 
5.9%
s 208829
 
5.9%
o 104898
 
3.0%
Other values (3) 313727
8.8%
Uppercase Letter
ValueCountFrequency (%)
R 208829
33.3%
W 208829
33.3%
D 104898
16.7%
S 103931
16.6%
Space Separator
ValueCountFrequency (%)
626487
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 208829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4178514
83.3%
Common 835316
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 522556
12.5%
n 522556
12.5%
e 521589
12.5%
i 418625
10.0%
d 312760
 
7.5%
R 208829
 
5.0%
a 208829
 
5.0%
l 208829
 
5.0%
u 208829
 
5.0%
s 208829
 
5.0%
Other values (7) 836283
20.0%
Common
ValueCountFrequency (%)
626487
75.0%
- 208829
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5013830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
626487
12.5%
t 522556
10.4%
n 522556
10.4%
e 521589
10.4%
i 418625
 
8.3%
d 312760
 
6.2%
R 208829
 
4.2%
a 208829
 
4.2%
l 208829
 
4.2%
u 208829
 
4.2%
Other values (9) 1253941
25.0%

Sample Duration
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1 HOUR
208586 
30 MINUTE
 
243

Length

Max length9
Median length6
Mean length6.0034909
Min length6

Characters and Unicode

Total characters1253703
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 HOUR
2nd row1 HOUR
3rd row1 HOUR
4th row1 HOUR
5th row1 HOUR

Common Values

ValueCountFrequency (%)
1 HOUR 208586
99.9%
30 MINUTE 243
 
0.1%

Length

2024-02-13T20:09:01.968164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-13T20:09:02.055701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 208586
49.9%
hour 208586
49.9%
30 243
 
0.1%
minute 243
 
0.1%

Most occurring characters

ValueCountFrequency (%)
208829
16.7%
U 208829
16.7%
1 208586
16.6%
H 208586
16.6%
O 208586
16.6%
R 208586
16.6%
3 243
 
< 0.1%
0 243
 
< 0.1%
M 243
 
< 0.1%
I 243
 
< 0.1%
Other values (3) 729
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 835802
66.7%
Decimal Number 209072
 
16.7%
Space Separator 208829
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 208829
25.0%
H 208586
25.0%
O 208586
25.0%
R 208586
25.0%
M 243
 
< 0.1%
I 243
 
< 0.1%
N 243
 
< 0.1%
T 243
 
< 0.1%
E 243
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 208586
99.8%
3 243
 
0.1%
0 243
 
0.1%
Space Separator
ValueCountFrequency (%)
208829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 835802
66.7%
Common 417901
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 208829
25.0%
H 208586
25.0%
O 208586
25.0%
R 208586
25.0%
M 243
 
< 0.1%
I 243
 
< 0.1%
N 243
 
< 0.1%
T 243
 
< 0.1%
E 243
 
< 0.1%
Common
ValueCountFrequency (%)
208829
50.0%
1 208586
49.9%
3 243
 
0.1%
0 243
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1253703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
208829
16.7%
U 208829
16.7%
1 208586
16.6%
H 208586
16.6%
O 208586
16.6%
R 208586
16.6%
3 243
 
< 0.1%
0 243
 
< 0.1%
M 243
 
< 0.1%
I 243
 
< 0.1%
Other values (3) 729
 
0.1%

Pollutant Standard
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing208829
Missing (%)100.0%
Memory size1.6 MiB
Distinct273
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Minimum2023-01-01 00:00:00
Maximum2023-09-30 00:00:00
2024-02-13T20:09:02.133440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:09:02.230162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Units of Measure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Degrees Compass
104898 
Knots
103931 

Length

Max length15
Median length15
Mean length10.023153
Min length5

Characters and Unicode

Total characters2093125
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKnots
2nd rowKnots
3rd rowKnots
4th rowKnots
5th rowKnots

Common Values

ValueCountFrequency (%)
Degrees Compass 104898
50.2%
Knots 103931
49.8%

Length

2024-02-13T20:09:02.312828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-13T20:09:02.383574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
degrees 104898
33.4%
compass 104898
33.4%
knots 103931
33.1%

Most occurring characters

ValueCountFrequency (%)
s 418625
20.0%
e 314694
15.0%
o 208829
10.0%
D 104898
 
5.0%
g 104898
 
5.0%
r 104898
 
5.0%
104898
 
5.0%
C 104898
 
5.0%
m 104898
 
5.0%
p 104898
 
5.0%
Other values (4) 416691
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1674500
80.0%
Uppercase Letter 313727
 
15.0%
Space Separator 104898
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 418625
25.0%
e 314694
18.8%
o 208829
12.5%
g 104898
 
6.3%
r 104898
 
6.3%
m 104898
 
6.3%
p 104898
 
6.3%
a 104898
 
6.3%
n 103931
 
6.2%
t 103931
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
D 104898
33.4%
C 104898
33.4%
K 103931
33.1%
Space Separator
ValueCountFrequency (%)
104898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1988227
95.0%
Common 104898
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 418625
21.1%
e 314694
15.8%
o 208829
10.5%
D 104898
 
5.3%
g 104898
 
5.3%
r 104898
 
5.3%
C 104898
 
5.3%
m 104898
 
5.3%
p 104898
 
5.3%
a 104898
 
5.3%
Other values (3) 311793
15.7%
Common
ValueCountFrequency (%)
104898
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2093125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 418625
20.0%
e 314694
15.0%
o 208829
10.0%
D 104898
 
5.0%
g 104898
 
5.0%
r 104898
 
5.0%
104898
 
5.0%
C 104898
 
5.0%
m 104898
 
5.0%
p 104898
 
5.0%
Other values (4) 416691
19.9%

Event Type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing208829
Missing (%)100.0%
Memory size1.6 MiB

Observation Count
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.706506
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:02.450924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q124
median24
Q324
95-th percentile24
Maximum48
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0882261
Coefficient of variation (CV)0.088086622
Kurtosis66.878126
Mean23.706506
Median Absolute Deviation (MAD)0
Skewness-4.1547214
Sum4950606
Variance4.3606881
MonotonicityNot monotonic
2024-02-13T20:09:02.529955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
24 197137
94.4%
23 3662
 
1.8%
22 1521
 
0.7%
21 897
 
0.4%
20 637
 
0.3%
16 420
 
0.2%
19 415
 
0.2%
18 410
 
0.2%
15 362
 
0.2%
14 343
 
0.2%
Other values (17) 3025
 
1.4%
ValueCountFrequency (%)
1 122
0.1%
2 118
0.1%
3 97
 
< 0.1%
4 133
0.1%
5 124
0.1%
6 142
0.1%
7 131
0.1%
8 217
0.1%
9 194
0.1%
10 279
0.1%
ValueCountFrequency (%)
48 241
 
0.1%
47 1
 
< 0.1%
31 1
 
< 0.1%
24 197137
94.4%
23 3662
 
1.8%
22 1521
 
0.7%
21 897
 
0.4%
20 637
 
0.3%
19 415
 
0.2%
18 410
 
0.2%

Observation Percent
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.669002
Minimum4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:02.615550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile96
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9758794
Coefficient of variation (CV)0.080834703
Kurtosis65.326211
Mean98.669002
Median Absolute Deviation (MAD)0
Skewness-7.6995132
Sum20604949
Variance63.614653
MonotonicityNot monotonic
2024-02-13T20:09:02.702128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
100 197378
94.5%
96 3662
 
1.8%
92 1521
 
0.7%
88 897
 
0.4%
83 637
 
0.3%
67 420
 
0.2%
79 415
 
0.2%
75 410
 
0.2%
63 362
 
0.2%
58 343
 
0.2%
Other values (16) 2784
 
1.3%
ValueCountFrequency (%)
4 122
0.1%
8 118
0.1%
13 97
 
< 0.1%
17 133
0.1%
21 124
0.1%
25 142
0.1%
29 131
0.1%
33 217
0.1%
38 194
0.1%
42 279
0.1%
ValueCountFrequency (%)
100 197378
94.5%
98 1
 
< 0.1%
96 3662
 
1.8%
92 1521
 
0.7%
88 897
 
0.4%
83 637
 
0.3%
79 415
 
0.2%
75 410
 
0.2%
71 320
 
0.2%
67 420
 
0.2%

Arithmetic Mean
Real number (ℝ)

Distinct36132
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.260691
Minimum0
Maximum355
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:02.779156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q13.9375
median31.291667
Q3187.4875
95-th percentile268.91667
Maximum355
Range355
Interquartile range (IQR)183.55

Descriptive statistics

Standard deviation101.1748
Coefficient of variation (CV)1.05105
Kurtosis-1.272787
Mean96.260691
Median Absolute Deviation (MAD)30.483334
Skewness0.48848888
Sum20102024
Variance10236.34
MonotonicityNot monotonic
2024-02-13T20:09:02.866178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4 115
 
0.1%
3.320833 104
 
< 0.1%
3.108333 103
 
< 0.1%
3.358333 102
 
< 0.1%
3 102
 
< 0.1%
2.454167 101
 
< 0.1%
2.7125 99
 
< 0.1%
2.8875 99
 
< 0.1%
2.3875 98
 
< 0.1%
2.925 98
 
< 0.1%
Other values (36122) 207808
99.5%
ValueCountFrequency (%)
0 70
< 0.1%
0.004167 2
 
< 0.1%
0.0125 2
 
< 0.1%
0.016667 3
 
< 0.1%
0.020833 1
 
< 0.1%
0.025 4
 
< 0.1%
0.033333 2
 
< 0.1%
0.0375 2
 
< 0.1%
0.041667 3
 
< 0.1%
0.05 1
 
< 0.1%
ValueCountFrequency (%)
355 1
< 0.1%
354.2 1
< 0.1%
354 1
< 0.1%
353.916667 1
< 0.1%
353.66 1
< 0.1%
352.083333 1
< 0.1%
351.958333 1
< 0.1%
351.708333 1
< 0.1%
351.695833 1
< 0.1%
351.6875 1
< 0.1%

1st Max Value
Real number (ℝ)

Distinct3125
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.49271
Minimum0
Maximum360
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:02.947899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6
Q17.7
median55
Q3328.4
95-th percentile358
Maximum360
Range360
Interquartile range (IQR)320.7

Descriptive statistics

Standard deviation153.26418
Coefficient of variation (CV)0.99204795
Kurtosis-1.8027768
Mean154.49271
Median Absolute Deviation (MAD)53.1
Skewness0.21474613
Sum32262559
Variance23489.909
MonotonicityNot monotonic
2024-02-13T20:09:03.035460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
359 3734
 
1.8%
358 3149
 
1.5%
357 2631
 
1.3%
356 2427
 
1.2%
4.3 2271
 
1.1%
5.2 2192
 
1.0%
6.9 2159
 
1.0%
355 2100
 
1.0%
354 1947
 
0.9%
7.8 1945
 
0.9%
Other values (3115) 184274
88.2%
ValueCountFrequency (%)
0 70
< 0.1%
0.1 3
 
< 0.1%
0.2 8
 
< 0.1%
0.3 11
 
< 0.1%
0.4 26
 
< 0.1%
0.5 7
 
< 0.1%
0.6 73
< 0.1%
0.7 17
 
< 0.1%
0.8 102
< 0.1%
0.9 38
 
< 0.1%
ValueCountFrequency (%)
360 1503
0.7%
359.9 149
 
0.1%
359.8 148
 
0.1%
359.7 136
 
0.1%
359.6 145
 
0.1%
359.5 129
 
0.1%
359.4 116
 
0.1%
359.3 147
 
0.1%
359.2 124
 
0.1%
359.1 135
 
0.1%

1st Max Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.069473
Minimum0
Maximum23
Zeros11276
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:03.113640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.6716626
Coefficient of variation (CV)0.55277165
Kurtosis-0.93005189
Mean12.069473
Median Absolute Deviation (MAD)5
Skewness-0.19283043
Sum2520456
Variance44.511081
MonotonicityNot monotonic
2024-02-13T20:09:03.183386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
14 12813
 
6.1%
15 12434
 
6.0%
13 12041
 
5.8%
16 11760
 
5.6%
23 11431
 
5.5%
0 11276
 
5.4%
12 11040
 
5.3%
11 10414
 
5.0%
17 10007
 
4.8%
10 9759
 
4.7%
Other values (14) 95854
45.9%
ValueCountFrequency (%)
0 11276
5.4%
1 7450
3.6%
2 6586
3.2%
3 6038
2.9%
4 5653
2.7%
5 5560
2.7%
6 5421
2.6%
7 6296
3.0%
8 7710
3.7%
9 8834
4.2%
ValueCountFrequency (%)
23 11431
5.5%
22 7665
3.7%
21 6996
3.4%
20 6730
3.2%
19 6667
3.2%
18 8248
3.9%
17 10007
4.8%
16 11760
5.6%
15 12434
6.0%
14 12813
6.1%

AQI
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing208829
Missing (%)100.0%
Memory size1.6 MiB

Method Code
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.577013
Minimum20
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:03.262038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median24
Q365
95-th percentile68
Maximum130
Range110
Interquartile range (IQR)45

Descriptive statistics

Standard deviation27.716707
Coefficient of variation (CV)0.63603963
Kurtosis0.82447296
Mean43.577013
Median Absolute Deviation (MAD)4
Skewness0.99965027
Sum9100144
Variance768.21585
MonotonicityNot monotonic
2024-02-13T20:09:03.324102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 100880
48.3%
65 22859
 
10.9%
68 17967
 
8.6%
66 16923
 
8.1%
61 12638
 
6.1%
24 9490
 
4.5%
63 8335
 
4.0%
67 7026
 
3.4%
127 4970
 
2.4%
130 2835
 
1.4%
Other values (5) 4906
 
2.3%
ValueCountFrequency (%)
20 100880
48.3%
21 724
 
0.3%
22 844
 
0.4%
24 9490
 
4.5%
60 2436
 
1.2%
61 12638
 
6.1%
63 8335
 
4.0%
65 22859
 
10.9%
66 16923
 
8.1%
67 7026
 
3.4%
ValueCountFrequency (%)
130 2835
 
1.4%
129 360
 
0.2%
127 4970
 
2.4%
69 542
 
0.3%
68 17967
8.6%
67 7026
 
3.4%
66 16923
8.1%
65 22859
10.9%
63 8335
 
4.0%
61 12638
6.1%

Method Name
Categorical

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
INSTRUMENTAL - VECTOR SUMMATION
100880 
Instrumental - RM Young Model 05305
22859 
Instrumental - RM Young Ultrasonic Anemometer Model 86004
17967 
Instrumental - RM Young Ultrasonic Anemometer Model 81000
16923 
Instrumental - Met One Sonic Anemometer Model 50.5
12638 
Other values (12)
37562 

Length

Max length60
Median length31
Mean length37.781276
Min length27

Characters and Unicode

Total characters7889826
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInstrumental - RM Young Model 05103
2nd rowInstrumental - RM Young Model 05103
3rd rowInstrumental - RM Young Model 05103
4th rowInstrumental - RM Young Model 05103
5th rowInstrumental - RM Young Model 05103

Common Values

ValueCountFrequency (%)
INSTRUMENTAL - VECTOR SUMMATION 100880
48.3%
Instrumental - RM Young Model 05305 22859
 
10.9%
Instrumental - RM Young Ultrasonic Anemometer Model 86004 17967
 
8.6%
Instrumental - RM Young Ultrasonic Anemometer Model 81000 16923
 
8.1%
Instrumental - Met One Sonic Anemometer Model 50.5 12638
 
6.1%
INSTRUMENTAL - VECTOR SUMMATION LEVEL 4 9490
 
4.5%
Instrumental - Climatronics 8335
 
4.0%
Instrumental - RM Young Model 05103 7026
 
3.4%
RM Young Ultrasonic Wind Sensor - Vector Average Data Logger 2835
 
1.4%
Instrumental - ACOUSTIC SOUNDER 2485
 
1.2%
Other values (7) 7391
 
3.5%

Length

2024-02-13T20:09:03.393156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
208829
18.6%
instrumental 205634
18.4%
vector 115133
10.3%
summation 111938
10.0%
model 77413
 
6.9%
rm 67610
 
6.0%
young 67610
 
6.0%
anemometer 47528
 
4.2%
ultrasonic 38085
 
3.4%
05305 22859
 
2.0%
Other values (24) 157318
14.0%

Most occurring characters

ValueCountFrequency (%)
911128
 
11.5%
M 496862
 
6.3%
T 457692
 
5.8%
n 377272
 
4.8%
e 355169
 
4.5%
N 345754
 
4.4%
I 323084
 
4.1%
R 298941
 
3.8%
t 296482
 
3.8%
A 282956
 
3.6%
Other values (33) 3744486
47.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3717016
47.1%
Lowercase Letter 2658078
33.7%
Space Separator 911128
 
11.5%
Decimal Number 382137
 
4.8%
Dash Punctuation 208829
 
2.6%
Other Punctuation 12638
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 496862
13.4%
T 457692
12.3%
N 345754
9.3%
I 323084
8.7%
R 298941
8.0%
A 282956
7.6%
U 274386
7.4%
S 255474
6.9%
E 253447
6.8%
O 247538
6.7%
Other values (6) 480882
12.9%
Lowercase Letter
ValueCountFrequency (%)
n 377272
14.2%
e 355169
13.4%
t 296482
11.2%
o 262278
9.9%
l 217480
8.2%
r 199023
7.5%
m 194602
7.3%
u 158821
6.0%
a 155066
5.8%
s 143984
 
5.4%
Other values (6) 297901
11.2%
Decimal Number
ValueCountFrequency (%)
0 159111
41.6%
5 80816
21.1%
8 34890
 
9.1%
4 30253
 
7.9%
3 29885
 
7.8%
1 25033
 
6.6%
6 17967
 
4.7%
2 4182
 
1.1%
Space Separator
ValueCountFrequency (%)
911128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 208829
100.0%
Other Punctuation
ValueCountFrequency (%)
. 12638
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6375094
80.8%
Common 1514732
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 496862
 
7.8%
T 457692
 
7.2%
n 377272
 
5.9%
e 355169
 
5.6%
N 345754
 
5.4%
I 323084
 
5.1%
R 298941
 
4.7%
t 296482
 
4.7%
A 282956
 
4.4%
U 274386
 
4.3%
Other values (22) 2866496
45.0%
Common
ValueCountFrequency (%)
911128
60.2%
- 208829
 
13.8%
0 159111
 
10.5%
5 80816
 
5.3%
8 34890
 
2.3%
4 30253
 
2.0%
3 29885
 
2.0%
1 25033
 
1.7%
6 17967
 
1.2%
. 12638
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7889826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
911128
 
11.5%
M 496862
 
6.3%
T 457692
 
5.8%
n 377272
 
4.8%
e 355169
 
4.5%
N 345754
 
4.4%
I 323084
 
4.1%
R 298941
 
3.8%
t 296482
 
3.8%
A 282956
 
3.6%
Other values (33) 3744486
47.5%

Local Site Name
Text

MISSING 

Distinct547
Distinct (%)0.3%
Missing4753
Missing (%)2.3%
Memory size1.6 MiB
2024-02-13T20:09:03.713190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length69
Median length50
Mean length20.188405
Min length2

Characters and Unicode

Total characters4119969
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPCI MET1
2nd rowPCI MET1
3rd rowPCI MET1
4th rowPCI MET1
5th rowPCI MET1
ValueCountFrequency (%)
36851
 
5.6%
park 13500
 
2.1%
np 12670
 
1.9%
site 10524
 
1.6%
school 7700
 
1.2%
road 7577
 
1.2%
st 7209
 
1.1%
of 5888
 
0.9%
plant 5692
 
0.9%
labadie 5332
 
0.8%
Other values (979) 543276
82.8%
2024-02-13T20:09:04.187215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
454017
 
11.0%
e 254333
 
6.2%
a 230711
 
5.6%
o 193482
 
4.7%
n 173498
 
4.2%
t 172379
 
4.2%
r 161802
 
3.9%
i 146815
 
3.6%
l 136142
 
3.3%
S 121022
 
2.9%
Other values (65) 2075768
50.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2054273
49.9%
Uppercase Letter 1423891
34.6%
Space Separator 454017
 
11.0%
Dash Punctuation 62963
 
1.5%
Decimal Number 57785
 
1.4%
Other Punctuation 54466
 
1.3%
Close Punctuation 6287
 
0.2%
Open Punctuation 6287
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 254333
12.4%
a 230711
11.2%
o 193482
9.4%
n 173498
8.4%
t 172379
8.4%
r 161802
 
7.9%
i 146815
 
7.1%
l 136142
 
6.6%
s 108289
 
5.3%
d 65746
 
3.2%
Other values (16) 411076
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 121022
 
8.5%
E 111702
 
7.8%
A 111097
 
7.8%
R 105449
 
7.4%
N 98353
 
6.9%
C 86194
 
6.1%
P 83153
 
5.8%
O 81698
 
5.7%
T 76665
 
5.4%
L 76527
 
5.4%
Other values (16) 472031
33.2%
Decimal Number
ValueCountFrequency (%)
2 12070
20.9%
1 11754
20.3%
0 7524
13.0%
4 7104
12.3%
5 5102
8.8%
3 4516
 
7.8%
7 3386
 
5.9%
6 2886
 
5.0%
9 2621
 
4.5%
8 822
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 16034
29.4%
, 11210
20.6%
" 9940
18.2%
/ 6004
 
11.0%
& 3966
 
7.3%
# 3570
 
6.6%
' 2840
 
5.2%
: 542
 
1.0%
; 360
 
0.7%
Space Separator
ValueCountFrequency (%)
454017
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 62963
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6287
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3478164
84.4%
Common 641805
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 254333
 
7.3%
a 230711
 
6.6%
o 193482
 
5.6%
n 173498
 
5.0%
t 172379
 
5.0%
r 161802
 
4.7%
i 146815
 
4.2%
l 136142
 
3.9%
S 121022
 
3.5%
E 111702
 
3.2%
Other values (42) 1776278
51.1%
Common
ValueCountFrequency (%)
454017
70.7%
- 62963
 
9.8%
. 16034
 
2.5%
2 12070
 
1.9%
1 11754
 
1.8%
, 11210
 
1.7%
" 9940
 
1.5%
0 7524
 
1.2%
4 7104
 
1.1%
) 6287
 
1.0%
Other values (13) 42902
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4119969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
454017
 
11.0%
e 254333
 
6.2%
a 230711
 
5.6%
o 193482
 
4.7%
n 173498
 
4.2%
t 172379
 
4.2%
r 161802
 
3.9%
i 146815
 
3.6%
l 136142
 
3.3%
S 121022
 
2.9%
Other values (65) 2075768
50.4%
Distinct563
Distinct (%)0.3%
Missing362
Missing (%)0.2%
Memory size1.6 MiB
2024-02-13T20:09:04.710428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length86
Median length68
Mean length31.114397
Min length4

Characters and Unicode

Total characters6486325
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJack Springs Rd
2nd rowJack Springs Rd
3rd rowJack Springs Rd
4th rowJack Springs Rd
5th rowJack Springs Rd
ValueCountFrequency (%)
road 29185
 
2.6%
st 28096
 
2.5%
rd 21014
 
1.9%
park 16794
 
1.5%
street 15058
 
1.4%
ave 15040
 
1.4%
14713
 
1.3%
south 12810
 
1.2%
of 12407
 
1.1%
n 11994
 
1.1%
Other values (1564) 933247
84.0%
2024-02-13T20:09:05.236460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
917078
 
14.1%
A 253536
 
3.9%
E 241957
 
3.7%
e 238064
 
3.7%
R 213663
 
3.3%
a 205009
 
3.2%
S 195721
 
3.0%
O 193287
 
3.0%
N 184374
 
2.8%
T 174738
 
2.7%
Other values (68) 3668898
56.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2592703
40.0%
Lowercase Letter 1858472
28.7%
Space Separator 917078
 
14.1%
Decimal Number 817118
 
12.6%
Other Punctuation 234858
 
3.6%
Open Punctuation 21185
 
0.3%
Close Punctuation 21185
 
0.3%
Dash Punctuation 18394
 
0.3%
Math Symbol 5332
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 253536
 
9.8%
E 241957
 
9.3%
R 213663
 
8.2%
S 195721
 
7.5%
O 193287
 
7.5%
N 184374
 
7.1%
T 174738
 
6.7%
L 135570
 
5.2%
I 131263
 
5.1%
C 116211
 
4.5%
Other values (16) 752383
29.0%
Lowercase Letter
ValueCountFrequency (%)
e 238064
12.8%
a 205009
11.0%
t 170031
9.1%
o 161628
8.7%
r 144480
 
7.8%
n 139081
 
7.5%
i 131469
 
7.1%
l 102885
 
5.5%
d 84325
 
4.5%
s 80730
 
4.3%
Other values (16) 400770
21.6%
Decimal Number
ValueCountFrequency (%)
0 149395
18.3%
1 129367
15.8%
5 94404
11.6%
3 89538
11.0%
2 88694
10.9%
4 65662
8.0%
6 55057
 
6.7%
7 52281
 
6.4%
9 49355
 
6.0%
8 43365
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 129521
55.1%
. 65456
27.9%
/ 10368
 
4.4%
" 9940
 
4.2%
& 8937
 
3.8%
: 6462
 
2.8%
' 2116
 
0.9%
# 1696
 
0.7%
@ 362
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 20213
95.4%
[ 972
 
4.6%
Close Punctuation
ValueCountFrequency (%)
) 20213
95.4%
] 972
 
4.6%
Space Separator
ValueCountFrequency (%)
917078
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18394
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4451175
68.6%
Common 2035150
31.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 253536
 
5.7%
E 241957
 
5.4%
e 238064
 
5.3%
R 213663
 
4.8%
a 205009
 
4.6%
S 195721
 
4.4%
O 193287
 
4.3%
N 184374
 
4.1%
T 174738
 
3.9%
t 170031
 
3.8%
Other values (42) 2380795
53.5%
Common
ValueCountFrequency (%)
917078
45.1%
0 149395
 
7.3%
, 129521
 
6.4%
1 129367
 
6.4%
5 94404
 
4.6%
3 89538
 
4.4%
2 88694
 
4.4%
4 65662
 
3.2%
. 65456
 
3.2%
6 55057
 
2.7%
Other values (16) 250978
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6486325
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
917078
 
14.1%
A 253536
 
3.9%
E 241957
 
3.7%
e 238064
 
3.7%
R 213663
 
3.3%
a 205009
 
3.2%
S 195721
 
3.0%
O 193287
 
3.0%
N 184374
 
2.8%
T 174738
 
2.7%
Other values (68) 3668898
56.6%

State Name
Categorical

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
California
30874 
Texas
21448 
Arizona
 
11734
Michigan
 
11574
Missouri
 
11048
Other values (42)
122151 

Length

Max length20
Median length13
Mean length7.7606415
Min length4

Characters and Unicode

Total characters1620647
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlabama
3rd rowAlabama
4th rowAlabama
5th rowAlabama

Common Values

ValueCountFrequency (%)
California 30874
 
14.8%
Texas 21448
 
10.3%
Arizona 11734
 
5.6%
Michigan 11574
 
5.5%
Missouri 11048
 
5.3%
Iowa 9074
 
4.3%
Colorado 9008
 
4.3%
Maryland 8712
 
4.2%
Nevada 8227
 
3.9%
Oregon 7185
 
3.4%
Other values (37) 79945
38.3%

Length

2024-02-13T20:09:05.377145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 30874
 
13.9%
texas 21448
 
9.7%
arizona 11734
 
5.3%
michigan 11574
 
5.2%
missouri 11048
 
5.0%
iowa 9074
 
4.1%
colorado 9008
 
4.1%
maryland 8712
 
3.9%
nevada 8227
 
3.7%
oregon 7185
 
3.2%
Other values (41) 92744
41.8%

Most occurring characters

ValueCountFrequency (%)
a 240126
14.8%
i 183865
 
11.3%
o 157599
 
9.7%
n 143612
 
8.9%
r 96983
 
6.0%
s 84003
 
5.2%
e 72464
 
4.5%
l 67413
 
4.2%
C 46209
 
2.9%
d 41101
 
2.5%
Other values (34) 487272
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1386220
85.5%
Uppercase Letter 221628
 
13.7%
Space Separator 12799
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 240126
17.3%
i 183865
13.3%
o 157599
11.4%
n 143612
10.4%
r 96983
 
7.0%
s 84003
 
6.1%
e 72464
 
5.2%
l 67413
 
4.9%
d 41101
 
3.0%
h 41099
 
3.0%
Other values (14) 257955
18.6%
Uppercase Letter
ValueCountFrequency (%)
C 46209
20.8%
M 39116
17.6%
I 23770
10.7%
T 23548
10.6%
N 17383
 
7.8%
A 16021
 
7.2%
W 15956
 
7.2%
O 15491
 
7.0%
G 5062
 
2.3%
D 4162
 
1.9%
Other values (9) 14910
 
6.7%
Space Separator
ValueCountFrequency (%)
12799
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1607848
99.2%
Common 12799
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 240126
14.9%
i 183865
 
11.4%
o 157599
 
9.8%
n 143612
 
8.9%
r 96983
 
6.0%
s 84003
 
5.2%
e 72464
 
4.5%
l 67413
 
4.2%
C 46209
 
2.9%
d 41101
 
2.6%
Other values (33) 474473
29.5%
Common
ValueCountFrequency (%)
12799
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1620647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 240126
14.8%
i 183865
 
11.3%
o 157599
 
9.7%
n 143612
 
8.9%
r 96983
 
6.0%
s 84003
 
5.2%
e 72464
 
4.5%
l 67413
 
4.2%
C 46209
 
2.9%
d 41101
 
2.5%
Other values (34) 487272
30.1%
Distinct290
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:05.707174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length15
Mean length7.2798175
Min length3

Characters and Unicode

Total characters1520237
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEscambia
2nd rowEscambia
3rd rowEscambia
4th rowEscambia
5th rowEscambia
ValueCountFrequency (%)
clark 8619
 
3.6%
inyo 6396
 
2.7%
maricopa 6294
 
2.6%
franklin 5332
 
2.2%
san 5120
 
2.1%
pima 3982
 
1.6%
harris 3946
 
1.6%
santa 3664
 
1.5%
barbara 3372
 
1.4%
luis 3004
 
1.2%
Other values (305) 191616
79.4%
2024-02-13T20:09:06.191575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 190903
 
12.6%
n 126078
 
8.3%
r 123519
 
8.1%
e 115859
 
7.6%
o 109860
 
7.2%
i 85823
 
5.6%
l 73679
 
4.8%
t 67687
 
4.5%
s 62283
 
4.1%
k 36920
 
2.4%
Other values (41) 527626
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1240136
81.6%
Uppercase Letter 241769
 
15.9%
Space Separator 35896
 
2.4%
Other Punctuation 2436
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 190903
15.4%
n 126078
10.2%
r 123519
10.0%
e 115859
9.3%
o 109860
8.9%
i 85823
 
6.9%
l 73679
 
5.9%
t 67687
 
5.5%
s 62283
 
5.0%
k 36920
 
3.0%
Other values (15) 247525
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 24658
 
10.2%
M 23927
 
9.9%
S 22622
 
9.4%
B 20338
 
8.4%
H 15956
 
6.6%
P 14891
 
6.2%
L 13404
 
5.5%
F 12699
 
5.3%
W 11896
 
4.9%
D 9820
 
4.1%
Other values (13) 71558
29.6%
Other Punctuation
ValueCountFrequency (%)
. 1352
55.5%
' 1084
44.5%
Space Separator
ValueCountFrequency (%)
35896
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1481905
97.5%
Common 38332
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 190903
 
12.9%
n 126078
 
8.5%
r 123519
 
8.3%
e 115859
 
7.8%
o 109860
 
7.4%
i 85823
 
5.8%
l 73679
 
5.0%
t 67687
 
4.6%
s 62283
 
4.2%
k 36920
 
2.5%
Other values (38) 489294
33.0%
Common
ValueCountFrequency (%)
35896
93.6%
. 1352
 
3.5%
' 1084
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1520237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 190903
 
12.6%
n 126078
 
8.3%
r 123519
 
8.1%
e 115859
 
7.6%
o 109860
 
7.2%
i 85823
 
5.6%
l 73679
 
4.8%
t 67687
 
4.5%
s 62283
 
4.1%
k 36920
 
2.4%
Other values (41) 527626
34.7%
Distinct322
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2024-02-13T20:09:06.603984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length50
Median length41
Mean length10.606798
Min length3

Characters and Unicode

Total characters2215007
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot in a city
2nd rowNot in a city
3rd rowNot in a city
4th rowNot in a city
5th rowNot in a city
ValueCountFrequency (%)
city 68281
 
15.0%
not 63927
 
14.0%
a 63927
 
14.0%
in 63927
 
14.0%
las 2916
 
0.6%
vegas 2916
 
0.6%
houston 2624
 
0.6%
tucson 2534
 
0.6%
phoenix 2494
 
0.5%
park 2118
 
0.5%
Other values (375) 180922
39.6%
2024-02-13T20:09:07.099009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
247757
 
11.2%
i 206784
 
9.3%
t 200830
 
9.1%
a 185782
 
8.4%
o 177278
 
8.0%
n 170635
 
7.7%
e 128100
 
5.8%
c 90281
 
4.1%
r 84790
 
3.8%
y 79408
 
3.6%
Other values (45) 643362
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1692059
76.4%
Uppercase Letter 261131
 
11.8%
Space Separator 247757
 
11.2%
Open Punctuation 6369
 
0.3%
Close Punctuation 6369
 
0.3%
Other Punctuation 1200
 
0.1%
Dash Punctuation 122
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 206784
12.2%
t 200830
11.9%
a 185782
11.0%
o 177278
10.5%
n 170635
10.1%
e 128100
7.6%
c 90281
 
5.3%
r 84790
 
5.0%
y 79408
 
4.7%
l 76524
 
4.5%
Other values (16) 291647
17.2%
Uppercase Letter
ValueCountFrequency (%)
N 69394
26.6%
C 20368
 
7.8%
P 16761
 
6.4%
M 13497
 
5.2%
S 13474
 
5.2%
B 12010
 
4.6%
L 11710
 
4.5%
D 10892
 
4.2%
H 10274
 
3.9%
R 10055
 
3.9%
Other values (14) 72696
27.8%
Space Separator
ValueCountFrequency (%)
247757
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6369
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6369
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1953190
88.2%
Common 261817
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 206784
 
10.6%
t 200830
 
10.3%
a 185782
 
9.5%
o 177278
 
9.1%
n 170635
 
8.7%
e 128100
 
6.6%
c 90281
 
4.6%
r 84790
 
4.3%
y 79408
 
4.1%
l 76524
 
3.9%
Other values (40) 552778
28.3%
Common
ValueCountFrequency (%)
247757
94.6%
( 6369
 
2.4%
) 6369
 
2.4%
. 1200
 
0.5%
- 122
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2215007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
247757
 
11.2%
i 206784
 
9.3%
t 200830
 
9.1%
a 185782
 
8.4%
o 177278
 
8.0%
n 170635
 
7.7%
e 128100
 
5.8%
c 90281
 
4.1%
r 84790
 
3.8%
y 79408
 
3.6%
Other values (45) 643362
29.0%

CBSA Name
Text

MISSING 

Distinct193
Distinct (%)0.1%
Missing23740
Missing (%)11.4%
Memory size1.6 MiB
2024-02-13T20:09:07.405685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length45
Median length34
Mean length22.353781
Min length8

Characters and Unicode

Total characters4137439
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFairbanks, AK
2nd rowFairbanks, AK
3rd rowFairbanks, AK
4th rowFairbanks, AK
5th rowFairbanks, AK
ValueCountFrequency (%)
ca 27510
 
5.8%
tx 19882
 
4.2%
az 11734
 
2.5%
mi 10128
 
2.1%
st 9884
 
2.1%
mo-il 9098
 
1.9%
louis 9098
 
1.9%
co 8160
 
1.7%
nv 7771
 
1.6%
vegas-henderson-paradise 7409
 
1.6%
Other values (300) 355391
74.7%
2024-02-13T20:09:07.777217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 314182
 
7.6%
290976
 
7.0%
o 272910
 
6.6%
e 241216
 
5.8%
n 238484
 
5.8%
- 220707
 
5.3%
r 205905
 
5.0%
, 185089
 
4.5%
s 184692
 
4.5%
i 172360
 
4.2%
Other values (46) 1810918
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2504494
60.5%
Uppercase Letter 922651
 
22.3%
Space Separator 290976
 
7.0%
Dash Punctuation 220707
 
5.3%
Other Punctuation 198611
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 314182
12.5%
o 272910
10.9%
e 241216
9.6%
n 238484
9.5%
r 205905
8.2%
s 184692
7.4%
i 172360
 
6.9%
l 164537
 
6.6%
t 154613
 
6.2%
d 104313
 
4.2%
Other values (15) 451282
18.0%
Uppercase Letter
ValueCountFrequency (%)
A 104603
 
11.3%
C 83274
 
9.0%
M 63857
 
6.9%
I 62468
 
6.8%
S 61955
 
6.7%
W 54563
 
5.9%
L 52385
 
5.7%
T 51284
 
5.6%
O 45275
 
4.9%
D 43131
 
4.7%
Other values (15) 299856
32.5%
Other Punctuation
ValueCountFrequency (%)
, 185089
93.2%
. 12736
 
6.4%
/ 424
 
0.2%
' 362
 
0.2%
Space Separator
ValueCountFrequency (%)
290976
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 220707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3427145
82.8%
Common 710294
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 314182
 
9.2%
o 272910
 
8.0%
e 241216
 
7.0%
n 238484
 
7.0%
r 205905
 
6.0%
s 184692
 
5.4%
i 172360
 
5.0%
l 164537
 
4.8%
t 154613
 
4.5%
A 104603
 
3.1%
Other values (40) 1373643
40.1%
Common
ValueCountFrequency (%)
290976
41.0%
- 220707
31.1%
, 185089
26.1%
. 12736
 
1.8%
/ 424
 
0.1%
' 362
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4137439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 314182
 
7.6%
290976
 
7.0%
o 272910
 
6.6%
e 241216
 
5.8%
n 238484
 
5.8%
- 220707
 
5.3%
r 205905
 
5.0%
, 185089
 
4.5%
s 184692
 
4.5%
i 172360
 
4.2%
Other values (46) 1810918
43.8%
Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Minimum2023-04-24 00:00:00
Maximum2023-10-26 00:00:00
2024-02-13T20:09:07.893172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:09:07.976385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-02-13T20:08:57.045911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.277099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.307092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.358932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.434592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.452310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.482368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.471473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.450278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.467140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:54.941507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.005874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.132507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.372586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.390891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.435809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.525309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.529816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.559088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.548694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.532803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.560932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.027591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.087098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.233351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.455801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.465440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.528879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.618976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.620513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.642807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.634388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.614103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.654112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.114933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.166603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.316675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.535518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.546525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.615930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.703068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.711117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.719286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.722720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.697475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.747711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.199181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.265383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.407933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.618661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.635330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.709372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.787090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.796098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.805177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.803952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.795830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.847352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.288577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.361262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.484754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.697472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.720362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.794284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.864364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.867655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.874994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.877482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.876935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.935942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.372088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.458755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.570738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.779898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.803441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.884204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.945509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.951063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.973878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.950555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.959611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:54.040923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.455627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.541779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.654624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.866817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.890064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.969592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.031378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.039153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.055678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.033641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.045407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:54.137148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.539055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.633753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.735563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:45.950006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.982239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.076466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.110696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.121897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.136923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.119264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.125262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:54.219773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.621152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.717045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.830794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.039666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.074548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.161134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.198397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.211678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.234370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.210802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.226407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:54.662489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.713017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.800698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:57.920005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.136456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.184504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.263040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.289217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.295115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.318306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.293802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.312698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:54.763765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.814815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.890674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:58.029874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:46.227371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:47.271008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:48.357206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:49.374226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:50.398353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:51.394404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:52.372348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:53.386291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:54.861157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:55.911204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-13T20:08:56.965639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-02-13T20:08:58.262072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-13T20:08:58.815312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-13T20:08:59.824102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

State CodeCounty CodeSite NumParameter CodePOCLatitudeLongitudeDatumParameter NameSample DurationPollutant StandardDate LocalUnits of MeasureEvent TypeObservation CountObservation PercentArithmetic Mean1st Max Value1st Max HourAQIMethod CodeMethod NameLocal Site NameAddressState NameCounty NameCity NameCBSA NameDate of Last Change
0153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-01KnotsNaN24100.01.9708334.314NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
1153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-02KnotsNaN24100.04.9041677.319NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
2153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-03KnotsNaN24100.07.28750010.713NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
3153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-04KnotsNaN24100.04.18750010.43NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
4153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-05KnotsNaN24100.03.5375007.715NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
5153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-06KnotsNaN24100.01.5583334.314NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
6153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-07KnotsNaN24100.02.4416675.115NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
7153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-08KnotsNaN24100.02.6875006.712NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
8153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-09KnotsNaN24100.04.1125007.28NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
9153100061103131.0921-87.5435NAD83Wind Speed - Resultant1 HOURNaN2023-01-10KnotsNaN24100.01.9625004.815NaN67Instrumental - RM Young Model 05103PCI MET1Jack Springs RdAlabamaEscambiaNot in a cityNaN2023-07-18
State CodeCounty CodeSite NumParameter CodePOCLatitudeLongitudeDatumParameter NameSample DurationPollutant StandardDate LocalUnits of MeasureEvent TypeObservation CountObservation PercentArithmetic Mean1st Max Value1st Max HourAQIMethod CodeMethod NameLocal Site NameAddressState NameCounty NameCity NameCBSA NameDate of Last Change
2088195639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-22Degrees CompassNaN24100.0165.375000219.011NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088205639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-23Degrees CompassNaN24100.0170.375000248.013NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088215639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-24Degrees CompassNaN24100.0195.458333324.08NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088225639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-25Degrees CompassNaN24100.0185.916667298.09NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088235639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-26Degrees CompassNaN24100.0182.333333312.011NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088245639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-27Degrees CompassNaN24100.0189.916667330.013NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088255639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-28Degrees CompassNaN24100.0211.000000339.016NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088265639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-29Degrees CompassNaN24100.0150.083333265.07NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088275639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-30Degrees CompassNaN24100.0206.500000236.03NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19
2088285639101361104144.373056-110.830833WGS84Wind Direction - Resultant1 HOURNaN2023-08-31Degrees CompassNaN24100.0163.708333227.012NaN20INSTRUMENTAL - VECTOR SUMMATIONYellowstone National Park - Old Faithful Snow LodgeYellowstone National Park - Old Faithful Snow LodgeWyomingTetonNot in a cityJackson, WY-ID2023-10-19